Mapping global research on expert systems

IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE COLLNET Journal of Scientometrics and Information Management Pub Date : 2023-01-01 DOI:10.47974/cjsim-2021-0103
Bisma Bashir, Fayaz Ahmad Loan
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Abstract

Purpose: The purpose of the study is to conduct a scientometric analysis of expert systems literature indexed in the Science Citation Index Expanded (SCIE) of the Web of Science database for a period of ten years (2011-2020). Design/Methodology: The study collected data from the Science Citation Index Expanded (SCIE) of the Web of Science (WoS) database from 2011-2020. The synonymous, broader and related terms of Expert Systems were selected from the Dewey Decimal Classification Scheme and Sears List of Subject Headings. A string of these terms was employed to retrieve data in the advanced search mode of the database. The harvested data was analyzed using scientometric techniques. Besides, the Microsoft Excel and VOSviewer software were used to represent and map the research productivity on expert systems. Findings: The results indicated inconsistent fluctuations in the annual number of publications from 396 in 2011 to 463 in 2020 and a decline in the number of citations from 8344 in 2011 to 520 in 2020. Further, it is divulged that China, the USA, and Spain are the three top countries contributing to expert system research published 16.36%, 14.96%, and 8.95% of literature respectively. While analysing institutional performance, the results revealed that most of the institutions are from China (6), followed by Iran (4), Spain (3) and India (2) /Malaysia (2) respectively. Further, by clustering the network map for keywords co-occurrence, it was found that expert systems, fuzzy logic, knowledge-based systems, machine learning, and artificial intelligence are the most common keywords and hence the hot topics of research in the area. Practical implications: The findings of the study may help researchers, information scientists and technologists to identify the research progress in the field of expert systems. Besides, it will help librarians to know the hot topics of research, prominent publications and prolific authors in expert systems. This will be helpful in the collection development of expert systems and artificial intelligence in libraries and information centres. Research limitations: The database studied for the work does not represent the total literary output available on expert systems as data in other databases like Scopus, Compendex, IEEE Xplore, arXiv, etc. haven’t been harvested. Originality/Value: The study is based on current literature on expert systems and will highlight new research trends in the said field.
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目的:本研究的目的是对Web of Science数据库SCIE收录的专家系统文献进行为期十年(2011-2020)的科学计量学分析。设计/方法:本研究收集了Web of Science (WoS)数据库2011-2020年的科学引文索引扩展(SCIE)数据。专家系统的同义,更广泛和相关的术语是从杜威十进分类方案和西尔斯主题标题列表中选择的。使用这些术语的字符串在数据库的高级搜索模式中检索数据。收集的数据使用科学计量学技术进行分析。此外,还利用Microsoft Excel和VOSviewer软件对专家系统的研究生产力进行了表示和映射。研究发现:论文年发表数从2011年的396篇波动到2020年的463篇,引用数从2011年的8344篇下降到2020年的520篇。此外,据透露,中国、美国和西班牙是对专家系统研究贡献最大的三个国家,分别发表了16.36%、14.96%和8.95%的文献。在分析机构绩效时,结果显示,大多数机构来自中国(6所),其次是伊朗(4所)、西班牙(3所)和印度(2所)/马来西亚(2所)。此外,通过对关键词共现的网络图进行聚类,发现专家系统、模糊逻辑、知识系统、机器学习和人工智能是最常见的关键词,因此是该领域的研究热点。实践意义:本研究的发现可以帮助研究人员、信息科学家和技术人员识别专家系统领域的研究进展。此外,它还可以帮助图书馆员了解专家系统的研究热点、知名出版物和高产作者。这将有助于图书馆和信息中心的专家系统和人工智能的收藏发展。研究限制:研究的数据库并不代表专家系统上可用的全部文献输出,因为其他数据库(如Scopus, Compendex, IEEE Xplore, arXiv等)中的数据尚未收获。原创性/价值:该研究基于当前专家系统的文献,并将突出该领域的新研究趋势。
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来源期刊
COLLNET Journal of Scientometrics and Information Management
COLLNET Journal of Scientometrics and Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-
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